Systems and methods for determining a total amount of carbon emissions produced by a vehicle

ABSTRACT

Method and system for determining total carbon emissions of a first vehicle are disclosed. For example, the method includes determining a first amount of carbon emissions produced during a commissioning stage of the first vehicle, collecting driving data for one or more trips made by the first vehicle during an operating stage of the first vehicle, determining a second amount of carbon emissions produced during the operating stage of the first vehicle based at least in part upon the driving data, determining a third amount of carbon emissions produced during a decommissioning stage of the first vehicle, and determining the total amount of carbon emissions produced during a life cycle of the first vehicle based at least upon the first amount of carbon emissions, the second amount of carbon emissions, and the third amount of carbon emissions.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of International Application No. PCT/US2021/023626, filed Mar. 23, 2021, which claims priority to U.S. Provisional Pat. Application No. 63/000,874, filed Mar. 27, 2020, the entire disclosures of which are incorporated by reference herein.

FIELD OF THE DISCLOSURE

Some embodiments of the present disclosure are directed to determining a total amount of carbon emissions produced by a vehicle. More particularly, certain embodiments of the present disclosure provide systems and methods for determining a total amount of carbon emissions produced during a life cycle of a vehicle. Merely by way of example, the present disclosure has been applied to determining a total amount of carbon emissions by determining carbon emissions produced during a commissioning stage, an operating stage, and a decommissioning stage of the vehicle. But it would be recognized that the present disclosure has much broader range of applicability.

BACKGROUND OF THE DISCLOSURE

Carbon emissions from vehicles represent a major contributor to climate change. While new vehicle technologies have been developed to curb carbon emissions, the continued use of vehicles for private transportation will cause the amount of carbon emissions to remain high or even increase. Hence it is highly desirable to develop more accurate techniques for determining a total amount of carbon emissions produced by each vehicle during its life cycle, which may be used for future remedial actions.

BRIEF SUMMARY OF THE DISCLOSURE

Some embodiments of the present disclosure are directed to determining a total amount of carbon emissions produced by a vehicle. More particularly, certain embodiments of the present disclosure provide methods and systems for determining a total amount of carbon emissions produced during a life cycle of a vehicle. Merely by way of example, the present disclosure has been applied to determining a total amount of carbon emissions by determining carbon emissions produced during a commissioning stage, an operating stage, and a decommissioning stage of the vehicle But it would be recognized that the present disclosure has much broader range of applicability.

According to certain embodiments, a method for determining total carbon emissions of a first vehicle includes determining a first amount of carbon emissions produced during a commissioning stage of the first vehicle. The method further includes collecting driving data for one or more trips made by the first vehicle during an operating stage of the first vehicle and determining a second amount of carbon emissions produced during the operating stage of the first vehicle based at least in part upon the driving data. The driving data includes information related to one or more driving behaviors of one or more drivers of the first vehicle. Also, the method includes determining a third amount of carbon emissions produced during a decommissioning stage of the first vehicle. Additionally, the method includes determining the total amount of carbon emissions produced during a life cycle of the first vehicle based at least upon the first amount of carbon emissions, the second amount of carbon emissions, and the third amount of carbon emissions. The life cycle of the first vehicle includes the commissioning stage of the first vehicle, the operating stage of the first vehicle, and the decommissioning stage of the first vehicle.

According to certain embodiments, a computing device for determining total carbon emissions of a first vehicle includes one or more processors and a memory that stores instructions for execution by the one or more processors. The instructions, when executed, cause the one or more processors to determine a first amount of carbon emissions produced during a commissioning stage of the first vehicle. Further, the instructions, when executed, cause the one or more processors to collect driving data for one or more trips made by the first vehicle during an operating stage of the first vehicle and determine a second amount of carbon emissions produced during the operating stage of the first vehicle based at least in part upon the driving data. The driving data includes information related to one or more driving behaviors of one or more drivers of the first vehicle. Also, the instructions, when executed, cause the one or more processors to determine a third amount of carbon emissions produced during a decommissioning stage of the first vehicle. Additionally, the instructions, when executed, cause the one or more processors to determine the total amount of carbon emissions produced during a life cycle of the first vehicle based at least upon the first amount of carbon emissions, the second amount of carbon emissions, and the third amount of carbon emissions. The life cycle of the first vehicle includes the commissioning stage of the first vehicle, the operating stage of the first vehicle, and the decommissioning stage of the first vehicle.

According to certain embodiments, a non-transitory computer-readable medium stores instructions for determining total carbon emissions of a first vehicle. The instructions are executed by one or more processors of a computing device. The non-transitory computer-readable medium includes instructions to determine a first amount of carbon emissions produced during a commissioning stage of the first vehicle. Further, the non-transitory computer-readable medium includes instructions to collect driving data for one or more trips made by the first vehicle during an operating stage of the first vehicle and determine a second amount of carbon emissions produced during the operating stage of the first vehicle based at least in part upon the driving data. The driving data includes information related to one or more driving behaviors of one or more drivers of the first vehicle. Also, the non-transitory computer-readable medium includes instructions to determine a third amount of carbon emissions produced during a decommissioning stage of the first vehicle. Additionally, the non-transitory computer-readable medium includes instructions to determine the total amount of carbon emissions produced during a life cycle of the first vehicle based at least upon the first amount of carbon emissions, the second amount of carbon emissions, and the third amount of carbon emissions. The life cycle of the first vehicle includes the commissioning stage of the first vehicle, the operating stage of the first vehicle, and the decommissioning stage of the first vehicle.

Depending upon the embodiment, one or more benefits may be achieved. These benefits and various additional objects, features and advantages of the present disclosure can be fully appreciated with reference to the detailed description and accompanying drawings that follow.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a simplified method for determining a total amount of carbon emissions produced by a vehicle according to certain embodiments of the present disclosure.

FIGS. 2A and 2B are a simplified method for determining a total amount of carbon emissions produced by a vehicle according to some embodiments of the present disclosure.

FIGS. 3A-3D are a simplified method for determining a total amount of carbon emissions produced by a vehicle according to certain embodiments of the present disclosure.

FIG. 4 is a simplified method for training an artificial neural network according to certain embodiments of the present disclosure.

FIG. 5 is a diagram showing a system for determining a total amount of carbon emissions produced by a vehicle according to certain embodiments of the present disclosure.

FIG. 6 is a simplified diagram showing a computing device, according to various embodiments of the present disclosure.

DETAILED DESCRIPTION OF THE DISCLOSURE

Some embodiments of the present disclosure are directed to determining a total amount of carbon emissions produced by a vehicle. More particularly, certain embodiments of the present disclosure provide methods and systems for determining a total amount of carbon emissions produced during a life cycle of a vehicle. Merely by way of example, the present disclosure has been applied to determining a total amount of carbon emissions by determining carbon emissions produced during a commissioning stage, an operating stage, and a decommissioning stage of the vehicle But it would be recognized that the present disclosure has much broader range of applicability.

I. One or More Methods for Determining a Total Amount of Carbon Emissions Produced By a Vehicle According to Certain Embodiments

FIG. 1 is a simplified diagram showing a method 100 for determining a total amount of carbon emissions produced by a vehicle according to certain embodiments of the present disclosure. This diagram is merely an example, which should not unduly limit the scope of the claims. One of ordinary skill in the art would recognize many variations, alternatives, and modifications. In the illustrative embodiment, the method 100 is performed by a computing device (e.g., a server 406). However, it should be appreciated that, in some embodiments, some of the method 100 is performed by any computing device (e.g., a mobile device 402).

The method 100 includes process 102 for determining a first amount of carbon emissions produced during a commissioning stage of a vehicle, process 104 for collecting driving data for one or more trips made by the vehicle during an operating stage of the vehicle, process 106 for determining a second amount of carbon emissions produced during the operating stage of the vehicle based at least in part upon the driving data, process 108 for determining a third amount of carbon emissions produced during a decommissioning stage of the vehicle, and process 110 for determining the total amount of carbon emissions produced during a life cycle of the vehicle based at least upon the first amount of carbon emissions, the second amount of carbon emissions, and the third amount of carbon emissions.

Although the above has been shown using a selected group of processes for the method, there can be many alternatives, modifications, and variations. For example, some of the processes may be expanded and/or combined. Other processes may be inserted to those noted above. Depending upon the embodiment, the sequence of processes may be interchanged with others replaced. For example, although the method 100 is described as performed by the computing device above, some or all processes of the method are performed by any computing device or a processor directed by instructions stored in memory. As an example, some or all processes of the method are performed according to instructions stored in a non-transitory computer-readable medium.

Specifically, at the process 102, the commissioning stage of the vehicle includes construction of the vehicle according to some embodiments. For example, the first amount of carbon emissions produced during the commissioning stage of the vehicle includes carbon emissions produced during construction (e.g., manufacturing) of the vehicle, including construction (e.g., manufacturing) of all the vehicle parts of the vehicle. As such, the first amount of carbon emissions produced during the commissioning stage of the vehicle depends in part upon a make, a type, and/or a model of the vehicle according to certain embodiments. Additionally or alternatively, in some embodiments, the first amount of carbon emissions produced during the commissioning stage of the vehicle includes carbon emissions produced during a transportation of the vehicle to a destination (e.g., a distributor or a location indicated by an owner of the vehicle). In such embodiments, the first amount of carbon emissions depends in part upon a transportation method that has been used to deliver the vehicle to the distributor or the owner of the vehicle. For example, the transportation method may include an auto transport trailer (also known as a car hauler), a train, a boat, and/or an airplane. It should be appreciated that, in some embodiments, the transportation method may be provided by a third party.

At the process 104, the driving data includes information related to one or more driving behaviors of one or more drivers of the vehicle. As an example, the one or more driving behaviors represent a manner in which the one or more drivers have operated the vehicle. For example, the driving behaviors indicate the one or more drivers’ driving habits and/or driving patterns associated with the vehicle. As discussed below, the driving data is used to determine carbon emissions generated by the vehicle during the operating stage of the vehicle according to some embodiments.

According to some embodiments, the driving data is received, obtained, or otherwise collected from one or more sensors associated with the vehicle. For example, the one or more sensors include any type and number of accelerometers, gyroscopes, magnetometers, location sensors (e.g., GPS sensors), tilt sensors, yaw rate sensors, speedometers, steering angle sensors, brake sensors, proximity detectors, and/or any other suitable sensors that measure vehicle state and/or operation. In certain embodiments, the one or more sensors are part of or located in the vehicle. In some embodiments, the one or more sensors are part of a computing device (e.g., a mobile device of the one or more drivers) that is connected to the vehicle while the vehicle is in operation. According to certain embodiments, the driving data is collected continuously or at predetermined time intervals. According to some embodiments, the driving data is collected based on a triggering event. For example, the driving data is collected when each sensor has acquired a threshold amount of sensor measurements. According to other embodiments, the driving data may be received, obtained, or otherwise collected from a server (e.g., a server 406) associated with an insurance provider.

At the process 106, the second amount of carbon emissions produced by the vehicle during the operating stage is determined based in part upon the driving data. In other words, the second amount of carbon emissions produced by the vehicle depends on how the one or more drivers of the vehicle operated the vehicle.

According to certain embodiments, the one or more vehicle parts of the vehicle may be replaced with one or more new replacement parts during the operating stage of the vehicle. For example, the replacement parts may include a tire, an air filter, a brake pad, a window, a bumper, and/or a battery. In such embodiments, the second amount of carbon emissions produced by the vehicle during the operating stage is determined based in part upon the driving data and replacement data. The replacement data includes information related to the one or more new replacement parts that replaced the one or more used vehicle parts of the vehicle. Accordingly, the second amount of carbon emissions also includes an amount of carbon emissions produced during construction of the new replacement parts based in part upon the replacement data. Additionally or alternatively, the second amount of carbon emissions further includes an amount of carbon emissions produced during a transportation of the new replacement parts based upon a transportation method that has been used to deliver the new replacement parts to the vehicle. Additionally or alternatively, an amount of carbon emissions produced during deconstruction of the one or more used vehicle parts of the vehicle.

At the process 108, the decommissioning stage of the vehicle includes deconstruction of the vehicle according to some embodiments. For example, the third amount of carbon emissions produced during the decommissioning stage of the vehicle includes carbon emissions produced during deconstruction (e.g., destroyed or recycled) of the vehicle, including deconstruction (e.g., destroyed or recycled) of all the vehicle parts of the vehicle. As such, the third amount of carbon emissions produced during the decommissioning stage of the vehicle depends in part upon a make, a type, and/or a model of the vehicle according to certain embodiments. Additionally or alternatively, in some embodiments, the third amount of carbon emissions produced during the decommissioning stage of the vehicle includes carbon emissions produced during a transportation of the vehicle to a vehicle dismantling place (e.g., junkyard, wrecking yard, auto dismantling yard, car spare parts supplier, auto or vehicle recycling). In such embodiments, the third amount of carbon emissions depends in part upon a transportation method that has been used to deliver the vehicle to the vehicle dismantling place.

At the process 110, the life cycle of the vehicle includes the commissioning stage, the operating stage, and the decommissioning stage of the vehicle. In other words, the life cycle of the vehicle represents from the manufacturing of the vehicle parts to the deconstruction of the vehicle.

FIGS. 2A and 2B are a simplified method for determining a total amount of carbon emissions produced by a vehicle according to certain embodiments of the present disclosure. This diagram is merely an example, which should not unduly limit the scope of the claims. One of ordinary skill in the art would recognize many variations, alternatives, and modifications. In the illustrative embodiment, the method 200 is performed by a computing device (e.g., a server 406). However, it should be appreciated that, in some embodiments, some of the method 200 is performed by any computing device (e.g., a mobile device 402).

The method 200 includes process 202 for determining carbon emissions produced during construction of a vehicle, process 204 for determining a transportation method that has been used to deliver the vehicle to a destination, process 206 for determining carbon emissions produced during transportation of the vehicle to the destination based upon the transportation method, process 208 for determining a first amount of carbon emissions produced during a commissioning stage of the vehicle, process 210 for collecting driving data for one or more trips made by the vehicle during an operating stage of the vehicle, process 212 for analyzing the driving data to determine one or more driving features for the one or more driving behaviors of the one or more drivers of the vehicle, process 214 for determining a second amount of carbon emissions produced during the operating stage of the vehicle based at least in part upon the driving features, process 216 for determining a third amount of carbon emissions produced during a decommissioning stage of the vehicle, and process 218 for determining the total amount of carbon emissions produced during a life cycle of the vehicle based at least upon the first amount of carbon emissions, the second amount of carbon emissions, and the third amount of carbon emissions.

Although the above has been shown using a selected group of processes for the method, there can be many alternatives, modifications, and variations. For example, some of the processes may be expanded and/or combined. Other processes may be inserted to those noted above. Depending upon the embodiment, the sequence of processes may be interchanged with others replaced. For example, although the method 200 is described as performed by the computing device above, some or all processes of the method are performed by any computing device or a processor directed by instructions stored in memory. As an example, some or all processes of the method are performed according to instructions stored in a non-transitory computer-readable medium.

Specifically, at the process 202, carbon emissions produced during construction (e.g., manufacturing) of the vehicle include carbon emissions produced during manufacturing of all the vehicle parts of the vehicle and carbon emissions produced during manufacturing of the vehicle using the vehicle parts according to some embodiments. As an example, the carbon emissions produced during the construction of the vehicle depend in part upon a make, a type, and/or a model of the vehicle. It should be appreciated that, in some embodiments, the carbon emissions produced during the construction of the vehicle may be provided by a third party. As an example, a car manufacturing company may provide such information.

Additionally, according to certain embodiments, the carbon emissions produced during construction of the vehicle further includes carbon emissions produced during a transportation of the vehicle parts to a destination location (e.g., a manufacturer) to make the vehicle. To do so, one or more transportation methods that have been used to deliver the vehicle parts to the destination location to make the vehicle are determined. For example, the vehicle parts may be transported to its destination location by an auto transport trailer (also known as a car hauler), a train, a boat, and/or an airplane. It should be appreciated that, in some embodiments, the one or more transportation methods may be provided by one or more third parties.

At the process 204, the transportation method that has been used to deliver the vehicle to a final destination location is determined. The final destination location may be a distributor, a dealership, and/or a location indicated by an owner of the vehicle. For example, the vehicle may be transported to the final destination location by an auto transport trailer (also known as a car hauler), a train, a boat, and/or an airplane. It should be appreciated that, in some embodiments, the transportation method may be provided by a third party.

At the process 206, the carbon emissions produced during the transportation of the vehicle to the destination (e.g., a distributor, a dealership, and/or an owner of the vehicle) is determined based upon the transportation method.

At the process 208, the first amount of carbon emissions produced during the commissioning stage of the vehicle includes carbon emissions produced during the construction of the vehicle and/or the transportation of the vehicle. As described above, the first amount of carbon emissions produced during the commissioning stage of the vehicle includes the carbon emissions produced during the manufacturing of all the vehicle parts that make up the vehicle, the carbon emissions produced during the transportation of the vehicle parts to a manufacturer of the vehicle, the carbon emissions produced during the manufacturing of the vehicle, and/or the carbon emissions produced during the transportation of the vehicle to the final destination.

At the process 210, the driving data includes information related to one or more driving behaviors of one or more drivers of the vehicle. As an example, the one or more driving behaviors represent a manner in which the one or more drivers have operated the vehicle. For example, the driving behaviors indicate the one or more drivers’ driving habits and/or driving patterns associated with the vehicle. As discussed below, the driving data is used to determine carbon emissions generated by the vehicle during the operating stage of the vehicle according to some embodiments.

According to certain embodiments, the driving data is received, obtained, or otherwise collected from one or more sensors associated with the vehicle. For example, the one or more sensors include any type and number of accelerometers, gyroscopes, magnetometers, location sensors (e.g., GPS sensors), tilt sensors, yaw rate sensors, speedometers, steering angle sensors, brake sensors, proximity detectors, and/or any other suitable sensors that measure vehicle state and/or operation. In certain embodiments, the one or more sensors are part of or located in the vehicle. In some embodiments, the one or more sensors are part of a computing device (e.g., a mobile device of the one or more drivers) that is connected to the vehicle while the vehicle is in operation. According to certain embodiments, the driving data are collected continuously or at predetermined time intervals. According to some embodiments, the driving data are collected based on a triggering event. For example, the driving data are collected when each sensor has acquired a threshold amount of sensor measurements. According to other embodiments, the driving data may be received, obtained, or otherwise collected from a server (e.g., a server 406) associated with an insurance provider.

At the process 212, the one or more driving features are related to a fuel consumption efficiency associated with the vehicle. According to some embodiments, the amount of carbon emissions generated by the vehicle is related to a fuel consumption efficiency of the one or more drivers of the vehicle. For example, the fuel consumption efficiency indicates how fuel has been consumed during the respective trip given one or more driving features of the one or more drivers. As an example, the one or more driving features indicate various driving maneuvers made by the one or more drivers that can have an impact on the amount of fuel consumed including braking (e.g., excessive braking, sudden braking, braking while reaching a turn, braking while driving in a turn), acceleration (e.g., rapid acceleration, prolonged acceleration, acceleration while driving in a turn, accelerating while exiting a turn), cornering (e.g., sharp turning, swerving), speeding (e.g., cruising, adopting speed limits), lane changing, tailgating, idling, timing of gear shifting, and/or other suitable maneuvers. According to some embodiments, the one or more driving features are classified by their level of severity (e.g., speed and duration at which a maneuver is performed).

According to certain embodiments, the one or more driving features include a first set of driving features that reduce the carbon emissions generated by the vehicle (e.g., increase the fuel consumption efficiency) and a second set of driving features that increase the carbon emissions generated by the vehicle (e.g., decrease the fuel consumption efficiency). For example, a type of maneuver belonging to the first set of driving features includes performing smooth acceleration at moderate rates which tends to increase the fuel consumption efficiency, thereby reducing the carbon emissions. As an example, a type of maneuver belonging to the second set of driving features includes using excessive braking which tends to decrease fuel consumption efficiency, thereby increasing the carbon emissions. For example, a type of maneuver belonging to the first set of driving features includes avoiding constant acceleration by remaining in one lane which tends to increase the fuel consumption efficiency, thereby reducing the carbon emissions. As an example, a type of maneuver belonging to the second set of driving features includes making unnecessary braking and acceleration by tailgating which tends to decrease fuel consumption efficiency, thereby increasing the carbon emissions.

According to certain embodiments, the driving data includes information related to time, distance, and/or a driving behavior associated with each trip made by the one or more drivers. As an example, the driving behaviors represent a manner in which the one or more drivers have operated the vehicle. For example, the driving behaviors indicate the one or more drivers’ driving habits and/or driving patterns associated with the vehicle. As discussed below, the driving data may be used to determine an amount of carbon emissions generated by the vehicle.

At the process 214, the second amount of carbon emissions produced by the vehicle during the operating stage is determined based in part upon the driving features. In other words, the second amount of carbon emissions produced by the vehicle depends on how the one or more drivers of the vehicle operated the vehicle.

According to certain embodiments, the one or more vehicle parts of the vehicle may be replaced with one or more new replacement parts during the operating stage of the vehicle. For example, the replacement parts may include a tire, an air filter, a brake pad, a window, a bumper, and/or a battery. In such embodiments, the second amount of carbon emissions produced by the vehicle during the operating stage is determined based in part upon the driving data and replacement data. The replacement data includes information related to the one or more new replacement parts that replaced the one or more used vehicle parts of the vehicle. Accordingly, the second amount of carbon emissions also includes an amount of carbon emissions produced during construction of the new replacement parts based in part upon the replacement data. Additionally or alternatively, the second amount of carbon emissions further includes an amount of carbon emissions produced during a transportation of the new replacement parts based upon a transportation method that has been used to deliver the new replacement parts to the vehicle. Additionally or alternatively, an amount of carbon emissions produced during deconstruction of the one or more used vehicle parts of the vehicle.

At the process 216, the third amount of carbon emissions produced by the vehicle during the decommissioning stage of the vehicle is determined. According to some embodiments, the decommissioning stage of the vehicle includes deconstruction of the vehicle. For example, the third amount of carbon emissions produced during the decommissioning stage of the vehicle includes carbon emissions produced during deconstruction (e.g., destroyed or recycled) of the vehicle, including deconstruction (e.g., destroyed or recycled) of all the vehicle parts of the vehicle. As such, the third amount of carbon emissions produced during the decommissioning stage of the vehicle depends in part upon a make, a type, and/or a model of the vehicle according to certain embodiments. Additionally or alternatively, in some embodiments, the third amount of carbon emissions produced during the decommissioning stage of the vehicle includes carbon emissions produced during a transportation of the vehicle to a vehicle dismantling place (e.g., junkyard, wrecking yard, auto dismantling yard, car spare parts supplier, auto or vehicle recycling). In such embodiments, the third amount of carbon emissions depends in part upon a transportation method that has been used to deliver the vehicle to the vehicle dismantling place.

At the process 218, the life cycle of the vehicle includes the commissioning stage, the operating stage, and the decommissioning stage of the vehicle. In other words, the life cycle of the vehicle represents from the manufacturing of the vehicle parts to the deconstruction of the vehicle.

FIGS. 3A-3D are a simplified method for determining a total amount of carbon emissions produced by a vehicle according to certain embodiments of the present disclosure. This diagram is merely an example, which should not unduly limit the scope of the claims. One of ordinary skill in the art would recognize many variations, alternatives, and modifications. In the illustrative embodiment, the method 300 is performed by a computing device (e.g., a server 406). However, it should be appreciated that, in some embodiments, some of the method 200 is performed by any computing device (e.g., a mobile device 402).

The method 300 includes process 302 for determining carbon emissions produced during construction of a current vehicle, process 304 for determining a transportation method that has been used to deliver the current vehicle to a destination, process 306 for determining carbon emissions produced during transportation of the current vehicle to the destination based upon the transportation method, process 308 for determining a first amount of carbon emissions produced during a commissioning stage of the current vehicle, process 310 for collecting driving data for one or more trips made by the current vehicle during an operating stage of the current vehicle, process 312 for analyzing the driving data to determine one or more driving features for the one or more driving behaviors of the one or more drivers of the current vehicle, process 314 for determining a second amount of carbon emissions produced during the operating stage of the current vehicle based at least in part upon the driving features, process 316 for determining a third amount of carbon emissions produced during a decommissioning stage of the current vehicle, process 318 for determining the total amount of carbon emissions produced during a life cycle of the current vehicle based at least upon the first amount of carbon emissions, the second amount of carbon emissions, and the third amount of carbon emissions, process 320 for determining whether the total amount of carbon emissions produced during the life cycle of the current vehicle exceeds a predetermined threshold, process 322 for determining in response to determining that the total amount of carbon emissions exceeds the predetermined threshold, a recommended vehicle that produces a predicted amount of carbon emissions during a life cycle of the recommended vehicle, process 324 for determining a fourth amount of carbon emissions produced during a commissioning stage of a recommended vehicle, process 326 for determining a fifth amount of carbon emissions produced during the operating stage of the recommended vehicle based at least in part upon the driving data, process 328 for determining a sixth amount of carbon emissions produced during a decommissioning stage of the recommended vehicle, process 330 for determining the predicted amount of carbon emissions produced during the life cycle of the recommended vehicle based at least upon the fourth amount of carbon emissions, the fifth amount of carbon emissions, and the sixth amount of carbon emissions, process 332 for presenting the recommended vehicle to a user, process 334 for receiving, in response to presenting the recommended vehicle to the user, a response from the user indicating that the user wants to purchase the recommended vehicle, and process 336 for providing, in response to receiving the response, at least one selected from a group consisting of one or more auto shops or dealerships near the user, an estimated cost for the recommended vehicle, and an estimated insurance premium for the recommended vehicle.

Although the above has been shown using a selected group of processes for the method, there can be many alternatives, modifications, and variations. For example, some of the processes may be expanded and/or combined. Other processes may be inserted to those noted above. Depending upon the embodiment, the sequence of processes may be interchanged with others replaced. For example, although the method 300 is described as performed by the computing device above, some or all processes of the method are performed by any computing device or a processor directed by instructions stored in memory. As an example, some or all processes of the method are performed according to instructions stored in a non-transitory computer-readable medium.

Specifically, at the process 302, carbon emissions produced during construction (e.g., manufacturing) of the current vehicle include carbon emissions produced during manufacturing of all the vehicle parts of the current vehicle and carbon emissions produced during manufacturing of the current vehicle using the vehicle parts according to some embodiments. As an example, the carbon emissions produced during the construction of the current vehicle depend in part upon a make, a type, and/or a model of the current vehicle. It should be appreciated that, in some embodiments, the carbon emissions produced during the construction of the current vehicle may be provided by a third party. As an example, a car manufacturing company may provide such information.

Additionally, according to certain embodiments, the carbon emissions produced during construction of the current vehicle further includes carbon emissions produced during a transportation of the vehicle parts to a destination location (e.g., a manufacturer) to make the current vehicle. To do so, one or more transportation methods that have been used to deliver the vehicle parts to the destination location to make the current vehicle are determined. For example, the vehicle parts may be transported to its destination location by an auto transport trailer (also known as a car hauler), a train, a boat, and/or an airplane. It should be appreciated that, in some embodiments, the one or more transportation methods may be provided by one or more third parties.

At the process 304, the transportation method that has been used to deliver the current vehicle to a final destination location is determined. The final destination location may be a distributor, a dealership, and/or a location indicated by an owner of the current vehicle. For example, the current vehicle may be transported to the final destination location by an auto transport trailer (also known as a car hauler), a train, a boat, and/or an airplane. It should be appreciated that, in some embodiments, the transportation method may be provided by a third party.

At the process 306, the carbon emissions produced during the transportation of the current vehicle to the destination (e.g., a distributor, a dealership, and/or an owner of the current vehicle) is determined based upon the transportation method.

At the process 308, the first amount of carbon emissions produced during the commissioning stage of the current vehicle includes carbon emissions produced during the construction of the current vehicle and/or the transportation of the current vehicle. As described above, the first amount of carbon emissions produced during the commissioning stage of the current vehicle includes the carbon emissions produced during the manufacturing of all the vehicle parts that make up the current vehicle, the carbon emissions produced during the transportation of the vehicle parts to a manufacturer of the current vehicle, the carbon emissions produced during the manufacturing of the current vehicle, and/or the carbon emissions produced during the transportation of the current vehicle to the final destination.

At the process 310, the driving data includes information related to one or more driving behaviors of one or more drivers of the current vehicle. As an example, the one or more driving behaviors represent a manner in which the one or more drivers have operated the current vehicle. For example, the driving behaviors indicate the one or more drivers’ driving habits and/or driving patterns associated with the current vehicle. As discussed below, the driving data is used to determine carbon emissions generated by the current vehicle during the operating stage of the current vehicle according to some embodiments.

According to certain embodiments, the driving data is received, obtained, or otherwise collected from one or more sensors associated with the current vehicle. For example, the one or more sensors include any type and number of accelerometers, gyroscopes, magnetometers, location sensors (e.g., GPS sensors), tilt sensors, yaw rate sensors, speedometers, steering angle sensors, brake sensors, proximity detectors, and/or any other suitable sensors that measure current vehicle state and/or operation. In certain embodiments, the one or more sensors are part of or located in the current vehicle. In some embodiments, the one or more sensors are part of a computing device (e.g., a mobile device of the one or more drivers) that is connected to the current vehicle while the current vehicle is in operation. According to certain embodiments, the driving data are collected continuously or at predetermined time intervals. According to some embodiments, the driving data are collected based on a triggering event. For example, the driving data are collected when each sensor has acquired a threshold amount of sensor measurements. According to other embodiments, the driving data may be received, obtained, or otherwise collected from a server (e.g., a server 406) associated with an insurance provider.

At the process 312, the one or more driving features are related to a fuel consumption efficiency associated with the current vehicle. According to some embodiments, the amount of carbon emissions generated by the current vehicle is related to a fuel consumption efficiency of the one or more drivers of the current vehicle. For example, the fuel consumption efficiency indicates how fuel has been consumed during the respective trip given one or more driving features of the one or more drivers. As an example, the one or more driving features indicate various driving maneuvers made by the one or more drivers that can have an impact on the amount of fuel consumed including braking (e.g., excessive braking, sudden braking, braking while reaching a turn, braking while driving in a turn), acceleration (e.g., rapid acceleration, prolonged acceleration, acceleration while driving in a turn, accelerating while exiting a turn), cornering (e.g., sharp turning, swerving), speeding (e.g., cruising, adopting speed limits), lane changing, tailgating, idling, timing of gear shifting, and/or other suitable maneuvers. According to some embodiments, the one or more driving features are classified by their level of severity (e.g., speed and duration at which a maneuver is performed).

According to certain embodiments, the one or more driving features include a first set of driving features that reduce the carbon emissions generated by the current vehicle (e.g., increase the fuel consumption efficiency) and a second set of driving features that increase the carbon emissions generated by the current vehicle (e.g., decrease the fuel consumption efficiency). For example, a type of maneuver belonging to the first set of driving features includes performing smooth acceleration at moderate rates which tends to increase the fuel consumption efficiency, thereby reducing the carbon emissions. As an example, a type of maneuver belonging to the second set of driving features includes using excessive braking which tends to decrease fuel consumption efficiency, thereby increasing the carbon emissions. For example, a type of maneuver belonging to the first set of driving features includes avoiding constant acceleration by remaining in one lane which tends to increase the fuel consumption efficiency, thereby reducing the carbon emissions. As an example, a type of maneuver belonging to the second set of driving features includes making unnecessary braking and acceleration by tailgating which tends to decrease fuel consumption efficiency, thereby increasing the carbon emissions.

According to certain embodiments, the driving data includes information related to time, distance, and/or a driving behavior associated with each trip made by the one or more drivers. As an example, the driving behaviors represent a manner in which the one or more drivers have operated the current vehicle. For example, the driving behaviors indicate the one or more drivers’ driving habits and/or driving patterns associated with the current vehicle. As discussed below, the driving data may be used to determine an amount of carbon emissions generated by the current vehicle.

At the process 314, the second amount of carbon emissions produced by the current vehicle during the operating stage is determined based in part upon the driving features. In other words, the second amount of carbon emissions produced by the current vehicle depends on how the one or more drivers of the current vehicle operated the current vehicle.

According to certain embodiments, the one or more vehicle parts of the current vehicle may be replaced with one or more new replacement parts during the operating stage of the current vehicle. For example, the replacement parts may include a tire, an air filter, a brake pad, a window, a bumper, and/or a battery. In such embodiments, the second amount of carbon emissions produced by the current vehicle during the operating stage is determined based in part upon the driving data and replacement data. The replacement data includes information related to the one or more new replacement parts that replaced the one or more used vehicle parts of the current vehicle. Accordingly, the second amount of carbon emissions also includes an amount of carbon emissions produced during construction of the new replacement parts based in part upon the replacement data. Additionally or alternatively, the second amount of carbon emissions further includes an amount of carbon emissions produced during a transportation of the new replacement parts based upon a transportation method that has been used to deliver the new replacement parts to the current vehicle. Additionally or alternatively, an amount of carbon emissions produced during deconstruction of the one or more used vehicle parts of the current vehicle.

At the process 316, the third amount of carbon emissions produced by the current vehicle during the decommissioning stage of the current vehicle is determined. According to some embodiments, the decommissioning stage of the current vehicle includes deconstruction of the current vehicle. For example, the third amount of carbon emissions produced during the decommissioning stage of the current vehicle includes carbon emissions produced during deconstruction (e.g., destroyed or recycled) of the current vehicle, including deconstruction (e.g., destroyed or recycled) of all the vehicle parts of the current vehicle. As such, the third amount of carbon emissions produced during the decommissioning stage of the current vehicle depends in part upon a make, a type, and/or a model of the current vehicle according to certain embodiments. Additionally or alternatively, in some embodiments, the third amount of carbon emissions produced during the decommissioning stage of the current vehicle includes carbon emissions produced during a transportation of the current vehicle to a vehicle dismantling place (e.g., junkyard, wrecking yard, auto dismantling yard, car spare parts supplier, auto or vehicle recycling). In such embodiments, the third amount of carbon emissions depends in part upon a transportation method that has been used to deliver the current vehicle to the vehicle dismantling place.

At the process 318, the life cycle of the current vehicle includes the commissioning stage, the operating stage, and the decommissioning stage of the current vehicle. In other words, the life cycle of the current vehicle represents from the manufacturing of the vehicle parts to the deconstruction of the current vehicle.

At the process 320, the predetermined threshold may be set by the owner of the current vehicle, the one or more drivers of the current vehicle, and/or an insurance provider associated with the current vehicle according to some embodiments.

At the process 322, the predicted amount of carbon emission for the recommended vehicle is less than the total amount of carbon emissions for the current vehicle. In other words, if the total amount of carbon emissions of the current vehicle exceeds the predetermined threshold, a new recommended vehicle that is predicted to produces less amount of carbon emissions during its life cycle is determined. For example, the predicted amount of carbon emissions of the new recommended vehicle represents a total amount of carbon emissions that would have been produced if the one or more drivers of the current vehicle were to drive the new recommended vehicle.

To do so, at the process 324, the fourth amount of carbon emissions produced during the commissioning stage of the recommended vehicle is determined. The fourth amount of carbon emissions produced during the commissioning stage of the recommended vehicle includes carbon emissions produced during the construction (e.g., manufacturing) of the recommended vehicle and/or the transportation of the recommended vehicle.

Specifically, the carbon emissions produced during the construction (e.g., manufacturing) of the recommended vehicle include the carbon emissions produced during the manufacturing of all the vehicle parts that make up the recommended vehicle, the carbon emissions produced during the transportation of the vehicle parts to a manufacturer of the recommended vehicle, and/or the carbon emissions produced during the manufacturing of the recommended vehicle. As an example, the carbon emissions produced during the construction of the recommended vehicle depend in part upon a make, a type, and/or a model of the current vehicle. It should be appreciated that, in some embodiments, the carbon emissions produced during the construction of the recommended vehicle may be provided by a third party. As an example, a car manufacturing company may provide such information.

Additionally, according to certain embodiments, the carbon emissions produced during construction of the recommended vehicle further includes carbon emissions produced during a transportation of the vehicle parts to a destination location (e.g., a manufacturer) to make the recommended vehicle. To do so, one or more transportation methods that have been used to deliver the vehicle parts to the destination location to make the recommended vehicle are determined. For example, the vehicle parts may be transported to its destination location by an auto transport trailer (also known as a car hauler), a train, a boat, and/or an airplane. It should be appreciated that, in some embodiments, the one or more transportation methods may be provided by one or more third parties.

At the process 326, the fifth amount of carbon emissions produced by the recommended vehicle during the operating stage is determined based in part upon the driving data. In other words, the fifth amount of carbon emissions represents an amount of carbon emissions that would have been produced if the one or more drivers of the current vehicle were to drive the new recommended vehicle. As described above, the driving data includes information related to one or more driving behaviors of the one or more drivers. As such, by using the same driving data of the one or more driving behaviors of the one or more drivers associated with the current vehicle, the one or more drivers driving habits and/or driving patterns of the one or more drivers are determined. Based upon the driving habits and/or driving patterns of the one or more drivers, a fuel consumption of the recommended vehicle during the operating stage is predicted according to certain embodiments.

At the process 328, the sixth amount of carbon emissions produced by the recommended vehicle during the decommissioning stage of the recommended vehicle is determined. According to some embodiments, the decommissioning stage of the recommended vehicle includes deconstruction of the recommended vehicle. For example, the sixth amount of carbon emissions produced during the decommissioning stage of the recommended vehicle includes carbon emissions produced during deconstruction (e.g., destroyed or recycled) of the recommended vehicle, including deconstruction (e.g., destroyed or recycled) of all the vehicle parts of the recommended vehicle. As such, the sixth amount of carbon emissions produced during the decommissioning stage of the recommended vehicle depends in part upon a make, a type, and/or a model of the recommended vehicle according to certain embodiments. Additionally or alternatively, in some embodiments, the sixth amount of carbon emissions produced during the decommissioning stage of the recommended vehicle includes carbon emissions produced during a transportation of the recommended vehicle to a vehicle dismantling place (e.g., junkyard, wrecking yard, auto dismantling yard, car spare parts supplier, auto or vehicle recycling). In such embodiments, the sixth amount of carbon emissions depends in part upon a transportation method that has been used to deliver the recommended vehicle to the vehicle dismantling place.

Accordingly, at the process 330, the predicted amount of carbon emissions produced during the life cycle of the recommended vehicle is determined based at least upon the fourth amount of carbon emissions, the fifth amount of carbon emissions, and the sixth amount of carbon emissions.

At the process 332, the recommended vehicle is presented to the user. As an example, the recommended vehicle is transmitted to a user’s mobile device. In some embodiments, more than one recommended vehicle may be determined and presented to the user. In the illustrative embodiment, the user is the owner of the current vehicle. However, in some embodiments, the user may include one or more drivers of the current vehicle. In such embodiments, the recommended vehicle may be transmitted to any one of the one or more drivers of the current vehicle.

At the process 334, in response to presenting the recommended vehicle to the user, a response may be received from the user indicating that the user wants to purchase the recommended vehicle or that the user is interest in purchasing the recommended vehicle.

At the process 336, in response to receiving the user’s indication that the user wants to purchase the recommended vehicle or that the user is interest in purchasing the recommended vehicle, additional information is provided to the user. For example, an estimated cost of the recommended vehicle and one or more locations of auto shops or dealerships near the user to purchase the recommended vehicle may be provided to the user. Additionally or alternatively, an estimated insurance premium for the recommended vehicle may be provided to the user according to certain embodiments.

II. One or More Methods for Training Artificial Neural Network According To Certain Embodiments

FIG. 4 is a simplified method for training an artificial neural network for determining an amount of fuel consumed by a vehicle according to some embodiments of the present disclosure. As described above, the amount of fuel consumed by a vehicle depends on vehicle information (e.g., a specific make, type, and/or model) and one or more driving features indicative of various driving maneuvers made by the one or more drivers of a respective vehicle. As such, the driving features are related to a fuel consumption efficiency of the respective vehicle and are used to determine an amount of carbon emissions produced by the respective vehicle. Since a different vehicle (e.g., different make and model) may produce different amounts of carbon emissions with the same driving features (e.g., the same driving data), the artificial neural network is trained to determine driving features (e.g., an amount of fuel consumption) of a particular vehicle based upon collected driving data.

This diagram is merely an example, which should not unduly limit the scope of the claims. One of ordinary skill in the art would recognize many variations, alternatives, and modifications. The method 600 includes process 602 for collecting sets of training data, process 604 for providing one set of training data to an artificial neural network for training, process 606 for analyzing the one set of training data to determine past driving features associated with a past fuel consumption of a respective vehicle, process 608 for generating an estimated past efficiency value related to the past fuel consumption, process 610 for comparing the estimated past efficiency value with an actual past efficiency value, process 612 for adjusting parameters related to the past driving features associated with the past fuel consumption in the artificial neural network, and process 614 for determining whether training of the artificial neural network has been completed. Although the above has been shown using a selected group of processes for the method, there can be many alternatives, modifications, and variations. For example, some of the processes may be expanded and/or combined. Other processes may be inserted to those noted above. Depending upon the embodiment, the sequence of processes may be interchanged with others replaced. For example, some or all processes of the method are performed by a computing device or a processor directed by instructions stored in memory. As an example, some or all processes of the method are performed according to instructions stored in a non-transitory computer-readable medium.

At the process 602, one or more sets of training data for one or more past vehicle trips made by one or more vehicles are collected according to some embodiments. For example, each set of training data is associated with a vehicle and includes vehicle information (e.g., a make, a type, and/or a model of the vehicle), past driving data related to a past driving behavior of the respective vehicle, and an actual past efficiency value related to a past fuel consumption of the respective vehicle. As an example, the one or more sets of training data are collected from various past vehicle trips made by the one or more vehicles that have already been made by users. In various embodiments, the one or more sets of training data are collected from sensors (e.g., one or more accelerometers, one or more gyroscopes, one or more magnetometers, and/or one or more GPS sensors) associated with respective vehicles operated by the users.

At the process 604, one set of training data in the one or more sets of training data is provided to the artificial neural network to train the artificial neural network according to certain embodiments. As an example, the artificial neural network is a convolutional neural network, a recurrent neural network, a modular neural network, or any other suitable type of neural network.

At the process 606, the past driving data of the one set of training data are analyzed by the artificial neural network to determine one or more past driving features associated with the past fuel consumption of the respective vehicle according to some embodiments. According to certain embodiments, the one or more past driving features indicate various past driving maneuvers (e.g., braking, acceleration, speeding, and/or cornering) that have impacted the amount of fuel consumed by the respective vehicle. For example, past driving maneuvers such as sudden braking and/or acceleration are considered to consume more fuel. As an example, past driving maneuvers such as smooth braking and/or acceleration at moderate rates are considered to consume less fuel.

At the process 608, the estimated past efficiency value related to the past fuel consumption by the respective vehicle is generated by the artificial neural network based at least in part upon the one or more past driving features according to certain embodiments. For example, in generating the estimated past efficiency value, one or more parameters related to the one or more past driving features associated with the past fuel consumption of the respective vehicle are calculated by the artificial neural network (e.g., weight values associated with various layers of connections in the artificial neural network).

At the process 610, the estimated past efficiency value is compared with the actual past efficiency value to determine an accuracy of the estimated past efficiency value according to some embodiments. According to certain embodiments, the accuracy is determined by using a loss function or a cost function for the one set of training data.

At the process 612, based at least in part upon the comparison, the one or more parameters related to the one or more past driving features associated with the past fuel consumption of the respective vehicle are adjusted by the artificial neural network. For example, the one or more parameters are adjusted in order to reduce (e.g., minimize) the loss function or the cost function.

At the process 614, a determination is made on whether the training has been completed according to certain embodiments. For example, training for the one set of training data is completed when the loss function or the cost function for the one set of training data is sufficiently reduced (e.g., minimized). As an example, training for the artificial neural network is completed when training for each of the one or more sets of training data is accomplished.

In some embodiments, if the process 614 determines that training of the artificial neural network is not yet completed, then the method 600 returns to the process 604 in an iterative manner until training is deemed to be completed.

In certain embodiments, if the process 614 determines that training of the artificial neural network is completed, then the method 600 for training the artificial neural network stops. In some examples, the artificial neural network that has been trained by the method 600 is used as a model by the process 106 of the method 100 as shown in FIG. 1 , the process 214 of the method 200 as shown in FIG. 2B, and/or the process 314 of the method 300 as shown in FIG. 3B. In certain examples, the trained artificial neural network possesses existing knowledge of which past driving features are desirable in terms of past fuel consumption efficiency. In some examples, the determined one or more past driving features relate to the one or more past user driving features in the process 312 of the method 300 as shown in FIG. 3B.

III. One or More Systems for Reducing Carbon Emissions By Recommending One or More Alternative Forms of Transportation According to Certain Embodiments

FIG. 5 is a simplified diagram showing a system for determining a total amount of carbon emissions produced by a vehicle according to certain embodiments of the present disclosure. This diagram is merely an example, which should not unduly limit the scope of the claims. One of ordinary skill in the art would recognize many variations, alternatives, and modifications. In the illustrative embodiment, the system 400 includes a mobile device 402, a network 404, and a server 406. Although the above has been shown using a selected group of components for the system, there can be many alternatives, modifications, and variations. For example, some of the components may be expanded and/or combined. Other components may be inserted to those noted above. Depending upon the embodiment, the arrangement of components may be interchanged with others replaced.

In various embodiments, the system 400 is used to implement the method 100, the method 200, and/or the method 300. According to certain embodiments, the mobile device 402 is communicatively coupled to the server 406 via the network 404. As an example, the mobile device 402 includes one or more processors 416 (e.g., a central processing unit (CPU), a graphics processing unit (GPU)), a memory 418 (e.g., random-access memory (RAM), read-only memory (ROM), flash memory), a communications unit 420 (e.g., a network transceiver), a display unit 422 (e.g., a touchscreen), and one or more sensors 424 (e.g., an accelerometer, a gyroscope, a magnetometer, a location sensor). For example, the one or more sensors 424 are configured to generate the driving data. According to some embodiments, the driving data are collected continuously, at predetermined time intervals, and/or based on a triggering event (e.g., when each sensor has acquired a threshold amount of sensor measurements).

In some embodiments, the mobile device 402 is operated by the user. For example, the user installs an application associated with an insurer on the mobile device 402 and allows the application to communicate with the one or more sensors 424 to collect data (e.g., the driving data). According to some embodiments, the application collects the data continuously, at predetermined time intervals, and/or based on a triggering event (e.g., when each sensor has acquired a threshold amount of sensor measurements). In certain embodiments, the data is used to determine an amount of carbon emissions generated by the user’s vehicle in the method 100, the method 200, and/or the method 300. As an example, the data represents the user’s driving behaviors. According to some embodiments, there may be other drivers that drives the user’s vehicle. In such embodiments, there may be multiple mobile devices (e.g., mobile devices of one or more drivers of the vehicle) that are in communication with the server 406.

According to certain embodiments, the collected data are stored in the memory 418 before being transmitted to the server 406 using the communications unit 422 via the network 404 (e.g., via a local area network (LAN), a wide area network (WAN), the Internet). In some embodiments, the collected data are transmitted directly to the server 406 via the network 404. In certain embodiments, the collected data are transmitted to the server 406 via a third party. For example, a data monitoring system stores any and all data collected by the one or more sensors 424 and transmits those data to the server 406 via the network 404 or a different network.

According to certain embodiments, the server 406 includes a processor 430 (e.g., a microprocessor, a microcontroller), a memory 432, a communications unit 434 (e.g., a network transceiver), and a data storage 436 (e.g., one or more databases). In some embodiments, the server 406 is a single server, while in certain embodiments, the server 406 includes a plurality of servers with distributed processing. As an example, in FIG. 5 , the data storage 436 is shown to be part of the server 406. In some embodiments, the data storage 436 is a separate entity coupled to the server 406 via a network such as the network 404. In certain embodiments, the server 406 includes various software applications stored in the memory 432 and executable by the processor 430. For example, these software applications include specific programs, routines, or scripts for performing functions associated with the method 100, the method 200, and/or the method 300. As an example, the software applications include general-purpose software applications for data processing, network communication, database management, web server operation, and/or other functions typically performed by a server.

According to various embodiments, the server 406 receives, via the network 404, the driving data collected by the one or more sensors 424 from the application using the communications unit 434 and stores the data in the data storage 436. For example, the server 406 then processes the data to perform one or more processes of the method 100, one or more processes of the method 200, and/or one or more processes of the method 300.

According to certain embodiments, the recommended vehicle in the method 300 is transmitted to the mobile device 402, via the network 404, to be provided (e.g., displayed) to the user via the display unit 422.

In some embodiments, one or more processes of the method 100, one or more processes of the method 200, and/or one or more processes of the method 300 are performed by the mobile device 402. For example, the processor 416 of the mobile device 402 analyzes the driving data collected by the one or more sensors 424 to perform one or more processes of the method 100, one or more processes of the method 200, and/or one or more processes of the method 300.

IV. One or More Computer Devices According to Various Embodiments

FIG. 6 is a simplified diagram showing a computer device 500, according to various embodiments of the present disclosure. This diagram is merely an example, which should not unduly limit the scope of the claims. One of ordinary skill in the art would recognize many variations, alternatives, and modifications. In some examples, the computer device 500 includes a processing unit 502, a memory unit 504, an input unit 506, an output unit 508, and a communication unit 510. In various examples, the computer device 500 is configured to be in communication with a user 520 and/or a storage device 522. In certain examples, the system computer device 500 is configured according to the system 400 of FIG. 5 to implement the method 100 of FIG. 1 , the method 200 of FIGS. 2A and 2B, and/or the method 300 of FIGS. 3A-3D. Although the above has been shown using a selected group of components, there can be many alternatives, modifications, and variations. In some examples, some of the components may be expanded and/or combined. Some components may be removed. Other components may be inserted to those noted above. Depending upon the embodiment, the arrangement of components may be interchanged with others replaced.

In various embodiments, the processing unit 502 is configured for executing instructions, such as instructions to implement the method 100 of FIG. 1 , the method 200 of FIGS. 2A and 2B, and/or the method 300 of FIGS. 3A-3D. In some embodiments, executable instructions may be stored in the memory unit 504. In some examples, the processing unit 502 includes one or more processing units (e.g., in a multi-core configuration). In certain examples, the processing unit 502 includes and/or is communicatively coupled to one or more modules for implementing the systems and methods described in the present disclosure. In some examples, the processing unit 502 is configured to execute instructions within one or more operating systems, such as UNIX, LINUX, Microsoft Windows®, etc. In certain examples, upon initiation of a computer-implemented method, one or more instructions is executed during initialization. In some examples, one or more operations is executed to perform one or more processes described herein. In certain examples, an operation may be general or specific to a particular programming language (e.g., C, C#, C++, Java, or other suitable programming languages, etc.). In various examples, the processing unit 502 is configured to be operatively coupled to the storage device 522, such as via an on-board storage unit 512.

In various embodiments, the memory unit 504 includes a device allowing information, such as executable instructions and/or other data to be stored and retrieved. In some examples, the memory unit 504 includes one or more computer readable media. In some embodiments, data stored in the memory unit 504 include computer readable instructions for providing a user interface, such as to the user 504, via the output unit 508. In some examples, a user interface includes a web browser and/or a client application. In various examples, a web browser enables one or more users, such as the user 504, to display and/or interact with media and/or other information embedded on a web page and/or a website. In certain examples, the memory unit 504 include computer readable instructions for receiving and processing an input, such as from the user 504, via the input unit 506. In certain examples, the memory unit 504 includes random access memory (RAM) such as dynamic RAM (DRAM) or static RAM (SRAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and/or non-volatile RAM (NVRAN).

In various embodiments, the input unit 506 is configured to receive input, such as from the user 504. In some examples, the input unit 506 includes a keyboard, a pointing device, a mouse, a stylus, a touch sensitive panel (e.g., a touch pad or a touch screen), a gyroscope, an accelerometer, a position detector (e.g., a Global Positioning System), and/or an audio input device. In certain examples, the input unit 506, such as a touch screen of the input unit, is configured to function as both the input unit and the output unit.

In various embodiments, the output unit 508 includes a media output unit configured to present information to the user 504. In some embodiments, the output unit 508 includes any component capable of conveying information to the user 504. In certain embodiments, the output unit 508 includes an output adapter, such as a video adapter and/or an audio adapter. In various examples, the output unit 508, such as an output adapter of the output unit, is operatively coupled to the processing unit 502 and/or operatively coupled to an presenting device configured to present the information to the user, such as via a visual display device (e.g., a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, a cathode ray tube (CRT) display, an “electronic ink” display, a projected display, etc.) or an audio display device (e.g., a speaker arrangement or headphones).

In various embodiments, the communication unit 510 is configured to be communicatively coupled to a remote device. In some examples, the communication unit 510 includes a wired network adapter, a wireless network adapter, a wireless data transceiver for use with a mobile phone network (e.g., Global System for Mobile communications (GSM), 3G, 4G, or Bluetooth), and/or other mobile data networks (e.g., Worldwide Interoperability for Microwave Access (WIMAX)). In certain examples, other types of short-range or long-range networks may be used. In some examples, the communication unit 510 is configured to provide email integration for communicating data between a server and one or more clients.

In various embodiments, the storage unit 512 is configured to enable communication between the computer device 500, such as via the processing unit 502, and an external storage device 522. In some examples, the storage unit 512 is a storage interface. In certain examples, the storage interface is any component capable of providing the processing unit 502 with access to the storage device 522. In various examples, the storage unit 512 includes an Advanced Technology Attachment (ATA) adapter, a Serial ATA (SATA) adapter, a Small Computer System Interface (SCSI) adapter, a RAID controller, a SAN adapter, a network adapter, and/or any other component capable of providing the processing unit 502 with access to the storage device 522.

In some examples, the storage device 522 includes any computer-operated hardware suitable for storing and/or retrieving data. In certain examples, the storage device 522 is integrated in the computer device 500. In some examples, the storage device 522 includes a database, such as a local database or a cloud database. In certain examples, the storage device 522 includes one or more hard disk drives. In various examples, the storage device is external and is configured to be accessed by a plurality of server systems. In certain examples, the storage device includes multiple storage units such as hard disks or solid state disks in a redundant array of inexpensive disks (RAID) configuration. In some examples, the storage device 522 includes a storage area network (SAN) and/or a network attached storage (NAS) system.

V. Examples of Machine Learning According to Certain Embodiments

According to some embodiments, a processor or a processing element may be trained using supervised machine learning and/or unsupervised machine learning, and the machine learning may employ an artificial neural network, which, for example, may be a convolutional neural network, a recurrent neural network, a deep learning neural network, a reinforcement learning module or program, or a combined learning module or program that learns in two or more fields or areas of interest. Machine learning may involve identifying and recognizing patterns in existing data in order to facilitate making predictions for subsequent data. Models may be created based upon example inputs in order to make valid and reliable predictions for novel inputs.

According to certain embodiments, machine learning programs may be trained by inputting sample data sets or certain data into the programs, such as images, object statistics and information, historical estimates, and/or actual repair costs. The machine learning programs may utilize deep learning algorithms that may be primarily focused on pattern recognition and may be trained after processing multiple examples. The machine learning programs may include Bayesian Program Learning (BPL), voice recognition and synthesis, image or object recognition, optical character recognition, and/or natural language processing. The machine learning programs may also include natural language processing, semantic analysis, automatic reasoning, and/or other types of machine learning.

According to some embodiments, supervised machine learning techniques and/or unsupervised machine learning techniques may be used. In supervised machine learning, a processing element may be provided with example inputs and their associated outputs and may seek to discover a general rule that maps inputs to outputs, so that when subsequent novel inputs are provided the processing element may, based upon the discovered rule, accurately predict the correct output. In unsupervised machine learning, the processing element may need to find its own structure in unlabeled example inputs.

VI. Examples of Certain Embodiments of the Present Disclosure

According to certain embodiments, a method for determining total carbon emissions of a first vehicle includes determining a first amount of carbon emissions produced during a commissioning stage of the first vehicle. The method further includes collecting driving data for one or more trips made by the first vehicle during an operating stage of the first vehicle and determining a second amount of carbon emissions produced during the operating stage of the first vehicle based at least in part upon the driving data. The driving data includes information related to one or more driving behaviors of one or more drivers of the first vehicle. Also, the method includes determining a third amount of carbon emissions produced during a decommissioning stage of the first vehicle. Additionally, the method includes determining the total amount of carbon emissions produced during a life cycle of the first vehicle based at least upon the first amount of carbon emissions, the second amount of carbon emissions, and the third amount of carbon emissions. The life cycle of the first vehicle includes the commissioning stage of the first vehicle, the operating stage of the first vehicle, and the decommissioning stage of the first vehicle. For example, the method is implemented according to at least FIG. 1 , FIGS. 2A and 2B, and/or FIGS. 3A-3D.

According to certain embodiments, a computing device for determining total carbon emissions of a first vehicle includes one or more processors and a memory that stores instructions for execution by the one or more processors. The instructions, when executed, cause the one or more processors to determine a first amount of carbon emissions produced during a commissioning stage of the first vehicle. Further, the instructions, when executed, cause the one or more processors to collect driving data for one or more trips made by the first vehicle during an operating stage of the first vehicle and determine a second amount of carbon emissions produced during the operating stage of the first vehicle based at least in part upon the driving data. The driving data includes information related to one or more driving behaviors of one or more drivers of the first vehicle. Also, the instructions, when executed, cause the one or more processors to determine a third amount of carbon emissions produced during a decommissioning stage of the first vehicle. Additionally, the instructions, when executed, cause the one or more processors to determine the total amount of carbon emissions produced during a life cycle of the first vehicle based at least upon the first amount of carbon emissions, the second amount of carbon emissions, and the third amount of carbon emissions. The life cycle of the first vehicle includes the commissioning stage of the first vehicle, the operating stage of the first vehicle, and the decommissioning stage of the first vehicle. For example, the computing device (e.g., the server 406) is implemented according to at least FIG. 5 .

According to certain embodiments, a non-transitory computer-readable medium stores instructions for determining total carbon emissions of a first vehicle. The instructions are executed by one or more processors of a computing device. The non-transitory computer-readable medium includes instructions to determine a first amount of carbon emissions produced during a commissioning stage of the first vehicle. Further, the non-transitory computer-readable medium includes instructions to collect driving data for one or more trips made by the first vehicle during an operating stage of the first vehicle and determine a second amount of carbon emissions produced during the operating stage of the first vehicle based at least in part upon the driving data. The driving data includes information related to one or more driving behaviors of one or more drivers of the first vehicle. Also, the non-transitory computer-readable medium includes instructions to determine a third amount of carbon emissions produced during a decommissioning stage of the first vehicle. Additionally, the non-transitory computer-readable medium includes instructions to determine the total amount of carbon emissions produced during a life cycle of the first vehicle based at least upon the first amount of carbon emissions, the second amount of carbon emissions, and the third amount of carbon emissions. The life cycle of the first vehicle includes the commissioning stage of the first vehicle, the operating stage of the first vehicle, and the decommissioning stage of the first vehicle. For example, the non-transitory computer-readable medium is implemented according to at least FIG. 1 , FIGS. 2A and 2B, and/or FIGS. 3A-3D.

VII. Additional Considerations According to Certain Embodiments

For example, some or all components of various embodiments of the present disclosure each are, individually and/or in combination with at least another component, implemented using one or more software components, one or more hardware components, and/or one or more combinations of software and hardware components. As an example, some or all components of various embodiments of the present disclosure each are, individually and/or in combination with at least another component, implemented in one or more circuits, such as one or more analog circuits and/or one or more digital circuits. For example, while the embodiments described above refer to particular features, the scope of the present disclosure also includes embodiments having different combinations of features and embodiments that do not include all of the described features. As an example, various embodiments and/or examples of the present disclosure can be combined.

Additionally, the methods and systems described herein may be implemented on many different types of processing devices by program code comprising program instructions that are executable by the device processing subsystem. The software program instructions may include source code, object code, machine code, or any other stored data that is operable to cause a processing system to perform the methods and operations described herein. Certain implementations may also be used, however, such as firmware or even appropriately designed hardware configured to perform the methods and systems described herein.

The systems’ and methods’ data (e.g., associations, mappings, data input, data output, intermediate data results, final data results) may be stored and implemented in one or more different types of computer-implemented data stores, such as different types of storage devices and programming constructs (e.g., RAM, ROM, EEPROM, Flash memory, flat files, databases, programming data structures, programming variables, IF-THEN (or similar type) statement constructs, application programming interface). It is noted that data structures describe formats for use in organizing and storing data in databases, programs, memory, or other computer-readable media for use by a computer program.

The systems and methods may be provided on many different types of computer-readable media including computer storage mechanisms (e.g., CD-ROM, diskette, RAM, flash memory, computer’s hard drive, DVD) that contain instructions (e.g., software) for use in execution by a processor to perform the methods’ operations and implement the systems described herein. The computer components, software modules, functions, data stores and data structures described herein may be connected directly or indirectly to each other in order to allow the flow of data needed for their operations. It is also noted that a module or processor includes a unit of code that performs a software operation, and can be implemented for example as a subroutine unit of code, or as a software function unit of code, or as an object (as in an object-oriented paradigm), or as an applet, or in a computer script language, or as another type of computer code. The software components and/or functionality may be located on a single computer or distributed across multiple computers depending upon the situation at hand.

The computing system can include mobile devices and servers. A mobile device and server are generally remote from each other and typically interact through a communication network. The relationship of mobile device and server arises by virtue of computer programs running on the respective computers and having a mobile device-server relationship to each other.

This specification contains many specifics for particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations, one or more features from a combination can in some cases be removed from the combination, and a combination may, for example, be directed to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

Although specific embodiments of the present disclosure have been described, it will be understood by those of skill in the art that there are other embodiments that are equivalent to the described embodiments. Accordingly, it is to be understood that the present disclosure is not to be limited by the specific illustrated embodiments. 

What is claimed is:
 1. A computer-implemented method for determining a total amount of carbon emissions of a first vehicle, the method comprising: determining, by a computing device, a first amount of carbon emissions produced during a commissioning stage of the first vehicle; collecting, by the computing device, driving data for one or more trips made by the first vehicle during an operating stage of the first vehicle, the driving data including information related to one or more driving behaviors of one or more drivers of the first vehicle; determining, by the computing device, a second amount of carbon emissions produced during the operating stage of the first vehicle based at least in part upon the driving data; determining, by the computing device, a third amount of carbon emissions produced during a decommissioning stage of the first vehicle; and determining, by the computing device, the total amount of carbon emissions produced during a life cycle of the first vehicle based at least upon the first amount of carbon emissions, the second amount of carbon emissions, and the third amount of carbon emissions; wherein the life cycle of the first vehicle includes the commissioning stage of the first vehicle, the operating stage of the first vehicle, and the decommissioning stage of the first vehicle.
 2. The computer-implemented method of claim 1, and further comprising: collecting, by the computing device, replacement data for replacing one or more first vehicle parts of the first vehicle with one or more second vehicle parts during the operating stage of the first vehicle; and determining, by the computing device, the second amount of carbon emissions produced during the operating stage of the first vehicle based at least in part upon the driving data and the replacement data.
 3. The method of claim 2, wherein the one or more second vehicle parts include at least one selected from a group consisting of a tire, an air filter, a brake pad, a window, a bumper, and a battery.
 4. The method of claim 2, wherein the determining the second amount of carbon emissions produced during the operating stage of the first vehicle based at least in part upon the driving data and the replacement data includes: determining, by the computing device, an amount of carbon emissions produced during construction of the one or more second vehicle parts; and determining, by the computing device, an amount of carbon emissions produced during a transportation of the one or more second vehicle parts based upon a transportation method that has been used to deliver the one or more second vehicle parts to the first vehicle.
 5. The method of claim 2, wherein the determining the second amount of carbon emissions produced during the operating stage of the first vehicle based at least in part upon the driving data and the replacement data includes determining an amount of carbon emissions produced during deconstruction of the one or more first vehicle parts.
 6. The method of claim 1, wherein the determining the first amount of carbon emissions produced during the commissioning stage includes: determining, by the computing device, a transportation method that has been used to deliver the first vehicle; and determining, by the computing device, carbon emissions produced during a transportation of the first vehicle to a destination based upon the transportation method.
 7. The method of claim 1, wherein the determining the second amount of carbon emissions produced during the operating stage includes: analyzing, by the computing device, the driving data to determine one or more driving features for the one or more driving behaviors of the one or more drivers of the first vehicle, the one or more driving features being related to a fuel consumption efficiency associated with the first vehicle; and determining, by the computing device, carbon emissions produced by the first vehicle based at least in part upon the one or more driving features.
 8. The method of 1, wherein: the determining the first amount of carbon emissions produced during the commissioning stage of the first vehicle includes determining the first amount of carbon emissions based at least in part upon a make, a type, and a model of the first vehicle; and the determining the third amount of carbon emissions produced during the decommissioning stage of the first vehicle includes determining the third amount of carbon emissions based at least in part upon the make, the type, and the model of the first vehicle.
 9. The method of claim 1, further comprising: determining, by the computing device, whether the total amount of carbon emissions produced during the life cycle of the first vehicle exceeds a predetermined threshold; and determining, in response to determining that the total amount of carbon emissions exceeds the predetermined threshold and by the computing device, a second vehicle that produces a predicted amount of carbon emissions during a life cycle of the second vehicle, the predicted amount of carbon emissions for the second vehicle being less than the total amount of carbon emissions for the first vehicle.
 10. The method of claim 9, wherein the determining the second vehicle includes: determining, by a computing device, a fourth amount of carbon emissions produced during a commissioning stage of the second vehicle; determining, by the computing device, a fifth amount of carbon emissions produced during the operating stage of the second vehicle based at least in part upon the driving data; determining, by the computing device, a sixth amount of carbon emissions produced during a decommissioning stage of the second vehicle; and determining, by the computing device, the predicted amount of carbon emissions produced during the life cycle of the second vehicle based at least upon the fourth amount of carbon emissions, the fifth amount of carbon emissions, and the sixth amount of carbon emissions.
 11. The method of claim 10, further comprising: recommending, by the computing device, the second vehicle to the one or more driver; receiving, in response to presenting the second vehicle to the user and by the computing device, a response from the user indicating that the user wants to purchase the second vehicle; and providing, in response to receiving the response and by the computing device, at least one selected from a group consisting of one or more auto shops or dealerships near the user, an estimated cost for the second vehicle, and an estimated insurance premium for the second vehicle.
 12. A computing device for determining a total amount of carbon emissions of a first vehicle, the computing device comprising: a processor; and a memory having a plurality of instructions stored thereon that, when executed by the processor, causes the computing device to: detect a first amount of carbon emissions produced during a commissioning stage of the first vehicle; collect driving data for one or more trips made by the first vehicle during an operating stage of the first vehicle, the driving data including information related to one or more driving behaviors of one or more drivers of the first vehicle; determine a second amount of carbon emissions produced during the operating stage of the first vehicle based at least in part upon the driving data; determine a third amount of carbon emissions produced during a decommissioning stage of the first vehicle; and determine the total amount of carbon emissions produced during a life cycle of the first vehicle based at least upon the first amount of carbon emissions, the second amount of carbon emissions, and the third amount of carbon emissions; wherein the life cycle of the first vehicle includes the commissioning stage of the first vehicle, the operating stage of the first vehicle, and the decommissioning stage of the first vehicle.
 13. The computing device of claim 12, wherein the plurality of instructions, when executed, further cause the computing device to: collect replacement data for replacing one or more first vehicle parts of the first vehicle with one or more second vehicle parts during the operating stage of the first vehicle; determine the second amount of carbon emissions produced during the operating stage of the first vehicle based at least in part upon the driving data and the replacement data.
 14. The computing device of claim 13, to determine the second amount of carbon emissions produced during the operating stage of the first vehicle based at least in part upon the driving data and the replacement data includes to determine, based at least in part upon the replacement data, (1) an amount of carbon emissions produced during construction of the one or more second vehicle parts, (2) an amount of carbon emissions produced during a transportation of the one or more second vehicle parts based upon a transportation method that has been used to deliver the one or more second vehicle parts to the first vehicle, and (3) an amount of carbon emissions produced during deconstruction of the one or more first vehicle parts.
 15. The computing device of claim 12, wherein to determine the first amount of carbon emissions produced during the commissioning stage includes to: determine a transportation method that has been used to deliver the first vehicle; and determine carbon emissions produced during a transportation of the first vehicle to a destination based upon the transportation method.
 16. The computing device of claim 12, wherein to determine the second amount of carbon emissions produced during the operating stage includes to: analyze the driving data to determine one or more driving features for the one or more driving behaviors of the one or more drivers of the first vehicle, the one or more driving features being related to a fuel consumption efficiency associated with the first vehicle; and determine carbon emissions produced by the first vehicle based at least in part upon the one or more driving features.
 17. The computing device of 12, wherein to: determine the first amount of carbon emissions produced during the commissioning stage of the first vehicle includes to determine the first amount of carbon emissions based at least in part upon a make, a type, and a model of the first vehicle; and determine the third amount of carbon emissions produced during the decommissioning stage of the first vehicle includes to determine the third amount of carbon emissions based at least in part upon the make, the type, and the model of the first vehicle.
 18. The computing device of claim 12, wherein the plurality of instructions, when executed, further cause the computing device to: determine whether the total amount of carbon emissions produced during the life cycle of the first vehicle exceeds a predetermined threshold; and determine, in response to determining that the total amount of carbon emissions exceeds the predetermined threshold, a second vehicle that produces a predicted amount of carbon emissions during a life cycle of the second vehicle, the predicted amount of carbon emissions for the second vehicle being less than the total amount of carbon emissions for the first vehicle.
 19. The computing device of claim 18, wherein to determine the second vehicle includes to: determine a fourth amount of carbon emissions produced during a commissioning stage of the second vehicle; determine a fifth amount of carbon emissions produced during the operating stage of the second vehicle based at least in part upon the driving data; determine a sixth amount of carbon emissions produced during a decommissioning stage of the second vehicle; and determine the predicted amount of carbon emissions produced during the life cycle of the second vehicle based at least upon the fourth amount of carbon emissions, the fifth amount of carbon emissions, and the sixth amount of carbon emissions.
 20. The computing device of claim 19, wherein the plurality of instructions, when executed, further cause the computing device to: recommend the second vehicle to the one or more driver; receive, in response to presenting the second vehicle to the user, a response from the user indicating that the user wants to purchase the second vehicle; and provide, in response to receiving the response, at least one selected from a group consisting of one or more auto shops or dealerships near the user, an estimated cost for the second vehicle, and an estimated insurance premium for the second vehicle. 